Graph Litmus Test¶

This is a very simple example to proove, that the GCN components are actually working.

In [1]:

            
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from Networkx_Dataset import get_random_graphs, plot_nx_edge_count
from photonai.base import Hyperpipe, PipelineElement
from sklearn.model_selection import KFold
import networkx as nx
import numpy as np
from Networkx_Dataset import get_random_graphs, plot_nx_edge_count
from photonai.base import Hyperpipe, PipelineElement
from sklearn.model_selection import KFold
import networkx as nx
import numpy as np

Create a sample dataset:

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# create dataset
sparse_graphs = get_random_graphs(500, 20, 0.2)
dense_graphs = get_random_graphs(500, 20, 0.4)
graphs = sparse_graphs + dense_graphs
graphs = np.stack([nx.to_numpy_array(g) for g in graphs])
graphs = np.repeat(graphs[:, :, :, np.newaxis], 2, axis=3)

# create labels
sparse_labels = [0] * 500
dense_labels = [1] * 500
labels = sparse_labels + dense_labels
# create dataset
sparse_graphs = get_random_graphs(500, 20, 0.2)
dense_graphs = get_random_graphs(500, 20, 0.4)
graphs = sparse_graphs + dense_graphs
graphs = np.stack([nx.to_numpy_array(g) for g in graphs])
graphs = np.repeat(graphs[:, :, :, np.newaxis], 2, axis=3)

# create labels
sparse_labels = [0] * 500
dense_labels = [1] * 500
labels = sparse_labels + dense_labels

Visualize the edge count of the graphs (we created two groups):

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plot_nx_edge_count(sparse_graphs, dense_graphs)
plot_nx_edge_count(sparse_graphs, dense_graphs)

Setup the PHOTONAI Pipeline

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my_pipe = Hyperpipe('basic_gcn_pipe',
                    inner_cv=KFold(n_splits=5),
                    outer_cv=KFold(n_splits=5),
                    optimizer='sk_opt',
                    optimizer_params={'n_configurations': 25},
                    metrics=['accuracy', 'balanced_accuracy', 'recall', 'precision'],
                    best_config_metric='accuracy')

my_pipe.add(PipelineElement('GraphConstructorThreshold', threshold=0.95))

my_pipe.add(PipelineElement('GCNClassifier', feature_axis=0))
my_pipe = Hyperpipe('basic_gcn_pipe',
                    inner_cv=KFold(n_splits=5),
                    outer_cv=KFold(n_splits=5),
                    optimizer='sk_opt',
                    optimizer_params={'n_configurations': 25},
                    metrics=['accuracy', 'balanced_accuracy', 'recall', 'precision'],
                    best_config_metric='accuracy')

my_pipe.add(PipelineElement('GraphConstructorThreshold', threshold=0.95))

my_pipe.add(PipelineElement('GCNClassifier', feature_axis=0))

Fit the Pipeline to our data:

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my_pipe.fit(graphs, labels)
my_pipe.fit(graphs, labels)

=====================================================================================================
PHOTONAI ANALYSIS: basic_gcn_pipe
=====================================================================================================
Found 2 target classes: [0 1]

*****************************************************************************************************
Outer Cross validation Fold 1
*****************************************************************************************************
Did not find any hyperparameter to convert into skopt space.

/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/optimization/scikit_optimize/sk_opt.py:169: UserWarning: Did not find any hyperparameter to convert into skopt space.
  warnings.warn(msg)
100%|██████████| 200/200 [00:37<00:00,  5.35it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
100%|██████████| 200/200 [00:36<00:00,  5.47it/s]
100%|██████████| 200/200 [00:34<00:00,  5.81it/s]
100%|██████████| 200/200 [00:35<00:00,  5.64it/s]
100%|██████████| 200/200 [00:34<00:00,  5.73it/s]

-----------------------------------------------------------------------------------------------------
BEST_CONFIG 
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
|       METRIC      | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
|      accuracy     |       0.9991      |      1.0000      |
| balanced_accuracy |       0.9985      |       nan        |
|       recall      |       0.9994      |      0.8000      |
|     precision     |       0.9992      |      0.8000      |
+-------------------+-------------------+------------------+

100%|██████████| 200/200 [00:43<00:00,  4.60it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
|       METRIC      | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
|      accuracy     |       1.0000      |      1.0000      |
| balanced_accuracy |       1.0000      |       nan        |
|       recall      |       1.0000      |      0.0000      |
|     precision     |       1.0000      |      0.0000      |
+-------------------+-------------------+------------------+

*****************************************************************************************************
Outer Cross validation Fold 2
*****************************************************************************************************
Did not find any hyperparameter to convert into skopt space.

/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/optimization/scikit_optimize/sk_opt.py:169: UserWarning: Did not find any hyperparameter to convert into skopt space.
  warnings.warn(msg)
100%|██████████| 200/200 [00:35<00:00,  5.59it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
100%|██████████| 200/200 [00:36<00:00,  5.46it/s]
100%|██████████| 200/200 [00:34<00:00,  5.78it/s]
100%|██████████| 200/200 [00:35<00:00,  5.67it/s]
100%|██████████| 200/200 [00:34<00:00,  5.83it/s]

-----------------------------------------------------------------------------------------------------
BEST_CONFIG 
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
|       METRIC      | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
|      accuracy     |       1.0000      |      1.0000      |
| balanced_accuracy |       1.0000      |       nan        |
|       recall      |       1.0000      |      0.8000      |
|     precision     |       1.0000      |      0.8000      |
+-------------------+-------------------+------------------+

100%|██████████| 200/200 [00:43<00:00,  4.63it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
|       METRIC      | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
|      accuracy     |       0.9975      |      1.0000      |
| balanced_accuracy |       0.9980      |       nan        |
|       recall      |       0.9960      |      0.0000      |
|     precision     |       1.0000      |      0.0000      |
+-------------------+-------------------+------------------+

*****************************************************************************************************
Outer Cross validation Fold 3
*****************************************************************************************************
Did not find any hyperparameter to convert into skopt space.

/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/optimization/scikit_optimize/sk_opt.py:169: UserWarning: Did not find any hyperparameter to convert into skopt space.
  warnings.warn(msg)
100%|██████████| 200/200 [00:34<00:00,  5.76it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
100%|██████████| 200/200 [00:34<00:00,  5.83it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
100%|██████████| 200/200 [00:34<00:00,  5.81it/s]
100%|██████████| 200/200 [00:33<00:00,  5.90it/s]
100%|██████████| 200/200 [00:33<00:00,  5.91it/s]

-----------------------------------------------------------------------------------------------------
BEST_CONFIG 
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
|       METRIC      | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
|      accuracy     |       0.9987      |      0.9975      |
| balanced_accuracy |       0.9986      |       nan        |
|       recall      |       0.9973      |      0.5988      |
|     precision     |       1.0000      |      0.6000      |
+-------------------+-------------------+------------------+

100%|██████████| 200/200 [00:42<00:00,  4.67it/s]

-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
|       METRIC      | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
|      accuracy     |       0.9975      |      1.0000      |
| balanced_accuracy |       0.9975      |      1.0000      |
|       recall      |       1.0000      |      1.0000      |
|     precision     |       0.9950      |      1.0000      |
+-------------------+-------------------+------------------+

*****************************************************************************************************
Outer Cross validation Fold 4
*****************************************************************************************************
Did not find any hyperparameter to convert into skopt space.

/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/optimization/scikit_optimize/sk_opt.py:169: UserWarning: Did not find any hyperparameter to convert into skopt space.
  warnings.warn(msg)
100%|██████████| 200/200 [00:34<00:00,  5.78it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
100%|██████████| 200/200 [00:34<00:00,  5.79it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
100%|██████████| 200/200 [00:35<00:00,  5.71it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
100%|██████████| 200/200 [00:33<00:00,  5.92it/s]
100%|██████████| 200/200 [00:33<00:00,  5.89it/s]

-----------------------------------------------------------------------------------------------------
BEST_CONFIG 
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
|       METRIC      | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
|      accuracy     |       1.0000      |      1.0000      |
| balanced_accuracy |       1.0000      |       nan        |
|       recall      |       1.0000      |      0.4000      |
|     precision     |       1.0000      |      0.4000      |
+-------------------+-------------------+------------------+

100%|██████████| 200/200 [00:42<00:00,  4.75it/s]

-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
|       METRIC      | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
|      accuracy     |       1.0000      |      1.0000      |
| balanced_accuracy |       1.0000      |       nan        |
|       recall      |       1.0000      |      1.0000      |
|     precision     |       1.0000      |      1.0000      |
+-------------------+-------------------+------------------+

*****************************************************************************************************
Outer Cross validation Fold 5
*****************************************************************************************************
Did not find any hyperparameter to convert into skopt space.

/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/optimization/scikit_optimize/sk_opt.py:169: UserWarning: Did not find any hyperparameter to convert into skopt space.
  warnings.warn(msg)
100%|██████████| 200/200 [00:34<00:00,  5.79it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
100%|██████████| 200/200 [00:34<00:00,  5.83it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
100%|██████████| 200/200 [00:33<00:00,  5.95it/s]
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
100%|██████████| 200/200 [00:33<00:00,  6.00it/s]
100%|██████████| 200/200 [00:33<00:00,  6.04it/s]

-----------------------------------------------------------------------------------------------------
BEST_CONFIG 
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
|       METRIC      | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
|      accuracy     |       0.9991      |      0.9963      |
| balanced_accuracy |       0.9991      |       nan        |
|       recall      |       1.0000      |      0.3987      |
|     precision     |       0.9980      |      0.4000      |
+-------------------+-------------------+------------------+

100%|██████████| 200/200 [00:42<00:00,  4.74it/s]

-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
|       METRIC      | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
|      accuracy     |       0.9988      |      1.0000      |
| balanced_accuracy |       0.9990      |       nan        |
|       recall      |       1.0000      |      1.0000      |
|     precision     |       0.9967      |      1.0000      |
+-------------------+-------------------+------------------+
*****************************************************************************************************
Finished all outer fold computations.

100%|██████████| 200/200 [00:53<00:00,  3.72it/s]

*****************************************************************************************************

ANALYSIS INFORMATION ================================================================================ 
Project Folder: /home/runner/work/photonai_graph/photonai_graph/documentation/docs/examples/basic_gcn_pipe_results_2023-02-17_16-12-10,
Computation Time: 2023-02-17 16:12:10.018930 - 2023-02-17 16:30:48.986392
Duration: 0:18:38.967462
Optimized for: accuracy
Hyperparameter Optimizer: sk_opt

DUMMY RESULTS =======================================================================================
+-------------------+--+
| PERFORMANCE DUMMY |  |
+-------------------+--+
+-------------------+--+

AVERAGE PERFORMANCE ACROSS OUTER FOLDS ==============================================================
+-------------------+---------------+--------------+-----------+----------+
|    Metric Name    | Training Mean | Training Std | Test Mean | Test Std |
+-------------------+---------------+--------------+-----------+----------+
|      accuracy     |    0.99875    |   0.001118   |    1.0    |   0.0    |
| balanced_accuracy |     0.9989    |   0.00102    |    nan    |   nan    |
|       recall      |     0.9992    |    0.0016    |    0.6    | 0.489898 |
|     precision     |    0.998341   |   0.002099   |    0.6    | 0.489898 |
+-------------------+---------------+--------------+-----------+----------+

BEST HYPERPARAMETER CONFIGURATION ===================================================================
{}


+--------+----------+-------------------+--------+-----------+----------------------------+
| fold # | accuracy | balanced_accuracy | recall | precision | Best Hyperparameter Config |
+--------+----------+-------------------+--------+-----------+----------------------------+
|   1*   |  1.0000  |        nan        | 0.0000 |   0.0000  |             {}             |
|   2    |  1.0000  |        nan        | 0.0000 |   0.0000  |             {}             |
|   3    |  1.0000  |       1.0000      | 1.0000 |   1.0000  |             {}             |
|   4    |  1.0000  |        nan        | 1.0000 |   1.0000  |             {}             |
|   5    |  1.0000  |        nan        | 1.0000 |   1.0000  |             {}             |
+--------+----------+-------------------+--------+-----------+----------------------------+

PHOTONAI 2.3.0 ======================================================================================
Your results are stored in /home/runner/work/photonai_graph/photonai_graph/documentation/docs/examples/basic_gcn_pipe_results_2023-02-17_16-12-10
Go to https://explorer.photon-ai.com and upload your photonai_results.json for convenient result visualization! 
For more info and documentation visit https://www.photon-ai.com

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/IPython/core/formatters.py:972, in MimeBundleFormatter.__call__(self, obj, include, exclude)
    969     method = get_real_method(obj, self.print_method)
    971     if method is not None:
--> 972         return method(include=include, exclude=exclude)
    973     return None
    974 else:

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/base.py:631, in BaseEstimator._repr_mimebundle_(self, **kwargs)
    629 output = {"text/plain": repr(self)}
    630 if get_config()["display"] == "diagram":
--> 631     output["text/html"] = estimator_html_repr(self)
    632 return output

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/utils/_estimator_html_repr.py:417, in estimator_html_repr(estimator)
    403 fallback_msg = (
    404     "In a Jupyter environment, please rerun this cell to show the HTML"
    405     " representation or trust the notebook. <br />On GitHub, the"
    406     " HTML representation is unable to render, please try loading this page"
    407     " with nbviewer.org."
    408 )
    409 out.write(
    410     f"<style>{style_with_id}</style>"
    411     f'<div id="{container_id}" class="sk-top-container">'
   (...)
    415     '<div class="sk-container" hidden>'
    416 )
--> 417 _write_estimator_html(
    418     out,
    419     estimator,
    420     estimator.__class__.__name__,
    421     estimator_str,
    422     first_call=True,
    423 )
    424 out.write("</div></div>")
    426 html_output = out.getvalue()

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/utils/_estimator_html_repr.py:151, in _write_estimator_html(out, estimator, estimator_label, estimator_label_details, first_call)
    149 """Write estimator to html in serial, parallel, or by itself (single)."""
    150 if first_call:
--> 151     est_block = _get_visual_block(estimator)
    152 else:
    153     with config_context(print_changed_only=True):

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/utils/_estimator_html_repr.py:127, in _get_visual_block(estimator)
    123 # check if estimator looks like a meta estimator wraps estimators
    124 if hasattr(estimator, "get_params"):
    125     estimators = [
    126         (key, est)
--> 127         for key, est in estimator.get_params(deep=False).items()
    128         if hasattr(est, "get_params") and hasattr(est, "fit")
    129     ]
    130     if estimators:
    131         return _VisualBlock(
    132             "parallel",
    133             [est for _, est in estimators],
    134             names=[f"{key}: {est.__class__.__name__}" for key, est in estimators],
    135             name_details=[str(est) for _, est in estimators],
    136         )

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/base.py:211, in BaseEstimator.get_params(self, deep)
    209 out = dict()
    210 for key in self._get_param_names():
--> 211     value = getattr(self, key)
    212     if deep and hasattr(value, "get_params") and not isinstance(value, type):
    213         deep_items = value.get_params().items()

AttributeError: 'Hyperpipe' object has no attribute 'best_config_metric'

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/IPython/core/formatters.py:342, in BaseFormatter.__call__(self, obj)
    340     method = get_real_method(obj, self.print_method)
    341     if method is not None:
--> 342         return method()
    343     return None
    344 else:

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/base.py:625, in BaseEstimator._repr_html_inner(self)
    620 def _repr_html_inner(self):
    621     """This function is returned by the @property `_repr_html_` to make
    622     `hasattr(estimator, "_repr_html_") return `True` or `False` depending
    623     on `get_config()["display"]`.
    624     """
--> 625     return estimator_html_repr(self)

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/utils/_estimator_html_repr.py:417, in estimator_html_repr(estimator)
    403 fallback_msg = (
    404     "In a Jupyter environment, please rerun this cell to show the HTML"
    405     " representation or trust the notebook. <br />On GitHub, the"
    406     " HTML representation is unable to render, please try loading this page"
    407     " with nbviewer.org."
    408 )
    409 out.write(
    410     f"<style>{style_with_id}</style>"
    411     f'<div id="{container_id}" class="sk-top-container">'
   (...)
    415     '<div class="sk-container" hidden>'
    416 )
--> 417 _write_estimator_html(
    418     out,
    419     estimator,
    420     estimator.__class__.__name__,
    421     estimator_str,
    422     first_call=True,
    423 )
    424 out.write("</div></div>")
    426 html_output = out.getvalue()

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/utils/_estimator_html_repr.py:151, in _write_estimator_html(out, estimator, estimator_label, estimator_label_details, first_call)
    149 """Write estimator to html in serial, parallel, or by itself (single)."""
    150 if first_call:
--> 151     est_block = _get_visual_block(estimator)
    152 else:
    153     with config_context(print_changed_only=True):

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/utils/_estimator_html_repr.py:127, in _get_visual_block(estimator)
    123 # check if estimator looks like a meta estimator wraps estimators
    124 if hasattr(estimator, "get_params"):
    125     estimators = [
    126         (key, est)
--> 127         for key, est in estimator.get_params(deep=False).items()
    128         if hasattr(est, "get_params") and hasattr(est, "fit")
    129     ]
    130     if estimators:
    131         return _VisualBlock(
    132             "parallel",
    133             [est for _, est in estimators],
    134             names=[f"{key}: {est.__class__.__name__}" for key, est in estimators],
    135             name_details=[str(est) for _, est in estimators],
    136         )

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/base.py:211, in BaseEstimator.get_params(self, deep)
    209 out = dict()
    210 for key in self._get_param_names():
--> 211     value = getattr(self, key)
    212     if deep and hasattr(value, "get_params") and not isinstance(value, type):
    213         deep_items = value.get_params().items()

AttributeError: 'Hyperpipe' object has no attribute 'best_config_metric'

Out[5]:

Hyperpipe(name='basic_gcn_pipe')

As we can see, the task is solved with an accuracy of nearly 1, test passed.